Bipartite Networks

  • What are they?
  • What are they good for?
  • How do I use them?

Bipartite Networks: What are they?

Definition: a network in which has two sets of nodes with edges between and not within sets.

Bipartite Networks: What are they?

Bipartite Networks: What are they?

Bipartite Networks: What are they?

Bipartite Networks: What are they?

mangal

Bipartite Networks: What are they good for?

  • Simplification (= ignore interactions within sets)
  • Stability Theory
  • Limitations

Bipartite Networks: Limitations

Bipartite Networks: How do I use them?

Can you represent your network as two distinct sets?

Bipartite Networks: How do I use them?

"Hello, R!"

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"Hello, R!"

2+2
## [1] 4

"Hello, R!"

four = 2 + 2

"Hello, R!"

four = 2 + 2

print(four)
## [1] 4

"Hello, bipartite!"

library(bipartite)

bipartite: models

Import/Load models using read.csv

people.fas.harvard.edu/~matthewklau/bipmodel.csv

bipartite: models

our.binet <- read.csv('../data/bipmodel.csv')

bipartite: nestedness

bipartite: nestedness

binet.nest <- nestedness(our.binet,null.models=FALSE)
names(binet.nest)
binet.nest$temperature

bipartite: nestedness

binet.nest <- nestedness(our.binet,null.models=TRUE,n.nulls=10)

bipartite: nestedness

plotweb(our.binet)

bipartite: modularity

bipartite: modularity

binet.mods <- computeModules(our.binet)
binet.mods
plotModuleWeb(binet.mods)

bipartite: modularity

null.nets <- nullmodel(our.binet, N=3, method="r2d")
null.mods <- lapply(null.nets,computeModules)
lapply(null.mods,slot,name='likelihood')

What does it all mean?

  • Structural analysis with dynamic implications
  • Nestedness minimizes competition and facilitates diversity (Bascompte 2003)
  • Modularity stabilizes through compartmentalization (Fortuna 2009)

Up Next!

Ecosystem networks and enaR!

Connect: the R network world

  • sna
  • network
  • statnet
  • igraph
  • graphviz
  • vegan

Connect: enaR to EcoNet

data(oyster)
write.EcoNet(oyster,file='./oyster.txt',mn='ena_model_oyster')